Recto-surface 3D segmentation U-Net (ps256)
Volumetric 3D U-Net that segments the recto writing surface of carbonized Herculaneum papyri directly in micro-CT volumes. It is the recto-surface predictor used in the pipeline behind "Complete virtual unwrapping and reading of a rolled Herculaneum papyrus" (Angelotti et al., arXiv:2606.29085, 2026).
Model details
| Architecture | nnU-Net-style 3D residual-encoder U-Net with concurrent spatial+channel squeeze-and-excitation (scSE) |
| Instantiated via | vesuvius NetworkFromConfig (config bundled as config.json) |
model_name |
ps256_bs2_msr_default |
| Input | 1-channel CT, 256 × 256 × 256 patches, z-score normalised |
| Output | 2 channels (surface vs background), ignore_label = 2 |
| Encoder | features_per_stage [32, 64, 128, 256, 320, 320, 320]; n_blocks_per_stage [1, 3, 4, 6, 6, 6, 6]; 3×3×3 kernels; stride-2 downsampling |
| Loss | Medial Surface Recall (Skeleton-Recall-style) + Dice/CE |
| Checkpoint | epoch 3504 · W&B run d5jdo9n1 |
Training data
Manually annotated recto surfaces (voxelised from surface meshes) of PHerc. 0139, 1667, 0343P, 0500P2, MAN Bp (~116.5k training / ~2.4k validation patches). Full training configuration is in Supplementary Table 1 of the paper.
Files
checkpoint_inference_ready.pth— full checkpoint; network weights are under keymodel(808 tensors). The architecture config is embedded undermodel_configand mirrored inconfig.json.config.json— architecture config forNetworkFromConfig.
How to load
import torch
ck = torch.load("checkpoint_inference_ready.pth", map_location="cpu", weights_only=False)
state = ck["model"] # state_dict
cfg = ck["model_config"] # == config.json
# Build the network with the vesuvius package (NetworkFromConfig(cfg)), then:
# net.load_state_dict(state)
The vesuvius package and inference code are in https://github.com/ScrollPrize/villa.
Links
- Paper: Angelotti et al., Complete virtual unwrapping and reading of a rolled Herculaneum papyrus. arXiv:2606.29085 (2026). https://arxiv.org/abs/2606.29085
- Code: https://github.com/ScrollPrize/villa
- Data: https://scrollprize.org/data_browser · ESRF: https://cultural-heritage.esrf.fr/tomo
- Vesuvius Challenge: https://scrollprize.org
Citation
@misc{angelotti2026unwrapping,
title = {Complete virtual unwrapping and reading of a rolled Herculaneum papyrus},
author = {Angelotti, Giorgio and others},
year = {2026},
eprint = {2606.29085},
archivePrefix = {arXiv},
primaryClass = {eess.IV},
doi = {10.48550/arXiv.2606.29085}
}
License
MIT — released by the Vesuvius Challenge. Note: the underlying tomographic data are distributed under CC BY-NC 4.0 (see the data links above).